Techniques and semantics in better training of deep learning models
I try to answer the things I know for sure:


One effect of bigger images is the increasing computation time due to more pixels (input to your training)


4.Grayscaling reduces the information, which might decrease training time, but also model performance (accuracy, precision, recall). What I have seen is that grayscaling is used in for example face detection where structural information (shapes) is important and colors can be regarded as secondary. For your example you have to determine whether such structural differences between positives and negatives exist and if color is a valuable information.


If possible, I would somehow alter these copies by clipping the objects (sun lights, lamps) and rotate, scale, make them transparent etc.
In the end your training data should be representative of the data it should generalize to. If you would for example take my user icon as an additional non fire example, this wouldn't help your cause.